Thermal control system

ABSTRACT

The subject matter of this specification can be embodied in, among other things, a method for time shifting when a cold storage facility is cooled that includes determining a thermal model of a cold storage facility, obtaining an energy cost model that describes a schedule of variable energy costs over a predetermined period of time in the future, determining an operational schedule for at least a portion of a refrigeration system based on the thermal model, the energy cost model, and a maximum allowed temperature, and powering on the portion the refrigeration system based on the operational schedule, cooling, by the powered portion of the refrigeration system to a temperature below the maximum allowed temperature, reducing power usage of the powered portion of the refrigeration system based on the operational schedule, and permitting the facility to be warmed by ambient temperatures toward the maximum allowed temperature.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Application Serial No.17/532,829, filed Nov. 22, 2021 which is a continuation of U.S.Application Serial No. 16/413,309, filed May 15, 2019, now Pat. No.11,181,316, issued Nov. 23, 2021, which is continuation of U.S.Application Serial No. 15/993,259, filed May 30, 2018, now patent number10,323,878, issued Jun. 18, 2019, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

This document generally relates to systems and techniques forrefrigeration management.

BACKGROUND

Cold storage facilities are used to cool and/or maintain stored content(e.g., inventory, food) at a reduced temperature. Cold storagefacilities range across a wide array of sizes, from small (e.g., walk-incoolers) to large (e.g., freezer warehouses). The temperature within acold storage facility is a result of a balance between heat removal fromand heat intrusion into the facility.

Heat intrusion within a cold storage facility can come from manydifferent sources, such as the environment (e.g., ambient airtemperature, solar radiation), the stored content (e.g., warm product tobe chilled), heat-producing equipment operating inside the facility(e.g., lights, forklifts), body heat from people working inside thefacility, and facility operations (e.g., opening of doors as people andinventory pass into and out of the facility).

The rate of heat intrusion can vary over time. Heat intrusion generallyincreases during the day as outdoor summer temperatures rise and as thesun rises to its peak midday intensity, and generally decreases asoutdoor summer temperatures and solar intensity fall. Heat intrusion canalso increase during times of high activity, such as during the workdaywhen doors are opened frequently, and decrease during times of lowactivity such as during after-hours when doors generally remain shut.

Heat removal from a cold storage facility generally requires theconsumption of power (e.g., electricity to drive refrigerationcompressors). As heat intrusion varies, so too does the need for powerto perform heat removal.

SUMMARY

This document generally describes systems and techniques for improvedrefrigeration management. For example, models of cold storagefacilities, such as refrigerated warehouses, can be generated and usedto determine the cooling strategies for more efficiently selecting timeswhen and temperatures to which the cold storage facilities are cooled.Cold storage facilities can be modeled as thermal batteries that arecapable of absorbing and storing thermal energy that can then bereleased over time to permit time shifting for when cooling occurs. Forexample, instead of cooling a cold storage facility as needed tomaintain a temperature or power draw setpoint, cold storage facilitiescan be cooled to a lower temperature than the setpoint and then thecooling systems can be modulated to consume less energy or be turned off(not consume energy) as the cold storage facility gradually warms(expends the stored thermal energy). The timing around when and setpoint to which a facility is cooled can depend on a variety of factors,such as the thermal model for a facility, which can model thermal effectof different usage of the facility (e.g., effect of facility doors beingopened/closed, effect of new items being added to the facility, effectof items begin removed from the facility), as well as external factors,such as the weather and solar load on the facility for a given day.

In a first aspect, a cold storage facility includes a cold storageenclosure defining an enclosed space, a refrigeration system configuredto cool the enclosed space, a plurality of temperature sensorsconfigured to sense temperature levels at a plurality of locationswithin the enclosed space, a control system including a data processingapparatus, a communication subsystem that transmits and receives dataover one or more networks and one or more media, a memory device storinginstructions that when executed by data processing apparatus cause thecontrol system to perform operations including determining a thermalmodel of the enclosed space based on temperature levels sensed by theplurality of temperature sensors, obtaining an energy cost model thatdescribes a schedule of variable energy costs over a predeterminedperiod of time in the future, determining an operational schedule for atleast a portion of the refrigeration system based on the thermal model,the energy cost model, and a maximum allowed temperature for theenclosed space, and powering on the portion the refrigeration systembased on the operational schedule, cooling, by the powered portion ofthe refrigeration system, the enclosed space to a temperature below themaximum allowed temperature, reducing power usage of the powered portionof the refrigeration system based on the operational schedule, andpermitting the enclosed space to be warmed by ambient temperaturestoward the maximum allowed temperature.

Various embodiments can include some, all, or none of the followingfeatures. The operations can also include determining a measuredtemperature of the enclosed space, and powering on at least a portion ofthe refrigeration system based on the determined measured temperatureand a predetermined threshold temperature value that is less than themaximum allowed temperature. The thermal model can be representative ofat least one of a thermal capacity of content within the enclosed space,and a thermal resistance of the cold storage enclosure. Determining anoperational schedule based on the thermal model, the energy cost model,and a maximum allowed temperature for the cold storage facility caninclude identifying, based on the energy cost model, a first period oftime during which energy costs a first amount per unit, identifying,based on the energy cost model, a second period of time preceding thefirst period of time, during which energy costs a second amount per unitthat is less than the first amount per unit, adding informationdescriptive of the second period of time to the operational schedule,the information being representative of time during which therefrigeration system is to be powered on to cool the enclosed spacebelow the maximum allowed temperature, and adding informationdescriptive of the first period of time to the operational schedule, theinformation being representative of time during which the enclosed spaceis allowed to warm toward the maximum allowed temperature. Determining athermal model of the enclosed space can include powering on the portionthe refrigeration system based on the operational schedule, cooling, bythe powered portion of the refrigeration system, the enclosed space to atemperature below the maximum allowed temperature, reducing power usageof the powered portion of the refrigeration system based on theoperational schedule, determining a first plurality of temperaturelevels sensed by the plurality of temperature sensors, permitting theenclosed space to be warmed by ambient temperatures toward the maximumallowed temperature, determining a second plurality of temperaturelevels sensed by the plurality of temperature sensors, and determining athermal capacity of content of the enclosed space. Determining a thermalmodel of the enclosed space can include powering on the portion therefrigeration system based on the operational schedule, cooling, by thepowered portion of the refrigeration system, the enclosed space to atemperature below the maximum allowed temperature, reducing power usageof the powered portion of the refrigeration system based on theoperational schedule, determining a first plurality of temperaturelevels sensed by the plurality of temperature sensors, permitting theenclosed space to be warmed by ambient temperatures toward the maximumallowed temperature, determining a second plurality of temperaturelevels sensed by the plurality of temperature sensors, and determining athermal capacity of content of the enclosed space.

In a second aspect, a cold storage management computer system forshifting times when a cold storage facility is cooled includes a dataprocessing apparatus, a communication subsystem that transmits andreceives data over one or more networks and one or more media, and amemory device storing instructions that when executed by data processingapparatus cause the user device to perform operations includingdetermining a thermal model of a cold storage facility comprising a coldstorage enclosure configured to be cooled by a refrigeration system anddefining an enclosed space, receiving, from a control system, a requestfor an operational schedule for at least a portion of the refrigerationsystem, obtaining an energy cost model that describes a schedule ofvariable energy costs over a predetermined period of time in the future,determining an operational schedule for at least a portion of therefrigeration system based on the thermal model, the energy cost model,and a maximum allowed temperature for the enclosed space, and providing,in response to the request, the operational schedule.

Various implementations can include some, all, or none of the followingfeatures. The operations can also include determining a measuredtemperature of the enclosed space, and powering on at least a portion ofthe refrigeration system based on the determined measured temperatureand a predetermined threshold temperature value that is less than themaximum allowed temperature. The thermal model can be representative ofat least one of the thermal capacity of content within the enclosedspace, and the thermal resistance of the cold storage enclosure.Determining an operational schedule based on the thermal model, theenergy cost model, and a maximum allowed temperature for the coldstorage facility can include identifying, based on the energy costmodel, a first period of time during which energy costs a first amountper unit, identifying, based on the energy cost model, a second periodof time preceding the first period of time, during which energy costs asecond amount per unit that is less than the first amount per unit,adding information descriptive of the second period of time to theoperational schedule, the information being representative of timeduring which the refrigeration system is to be powered on to cool theenclosed space below the maximum allowed temperature, and addinginformation descriptive of the first period of time to the operationalschedule, the information being representative of time during which theenclosed space is allowed to warm toward the maximum allowedtemperature. Determining a thermal model of the enclosed space caninclude powering on the portion the refrigeration system based on theoperational schedule, cooling, by the powered portion of therefrigeration system, the enclosed space to a temperature below themaximum allowed temperature, reducing power usage of the powered portionof the refrigeration system based on the operational schedule,determining a first plurality of temperature levels sensed by aplurality of temperature sensors, permitting the enclosed space to bewarmed by ambient temperatures toward the maximum allowed temperature,determining a second plurality of temperature levels sensed by theplurality of temperature sensors, and determining a thermal capacity ofcontent of the enclosed space.

In a third aspect, a cold storage control system for controlling coolingof a cold storage facility includes a data processing apparatus, acommunication subsystem that transmits and receives data over one ormore networks and one or more media, one or more input ports configuredto receive sensor signals from a plurality of temperature sensorsconfigured to sense temperature levels at a plurality of locationswithin a cold storage enclosure defining an enclosed space, one or moreoutput ports configured to trigger operation of a refrigeration systemconfigured to cool the enclosed space, a memory device storinginstructions that when executed by data processing apparatus cause thecold storage control system to perform operations includingtransmitting, over the one or more networks, a request for anoperational schedule for at least a portion of the refrigeration system,receiving, in response to the request, the operational schedule based ona thermal model, an energy cost model, and a maximum allowed temperaturefor the enclosed space, the operational schedule comprising informationthat is descriptive of a first period of time and a second period oftime that proceeds the first period of time, powering on the portion therefrigeration system at a start time of the second period of time,cooling, by the powered portion of the refrigeration system, theenclosed space to a temperature below the maximum allowed temperatureduring the second period of time, reducing power usage of the poweredportion of the refrigeration system at a start time of the first periodof time, and permitting the enclosed space to be warmed by ambienttemperatures toward the maximum allowed temperature during the firstperiod of time.

Various embodiments can include some, all, or none of the followingfeatures. The cold storage control system can also include determiningthat at least a portion of the enclosed space has warmed to at least apredetermined threshold temperature value that is less than the maximumallowed temperature, overriding the operational schedule by powering onthe portion the refrigeration system during the first period of time.The operations can also include determining a measured temperature ofthe enclosed space, and powering on at least a portion of therefrigeration system based on the determined measured temperature and apredetermined threshold temperature value that is less than the maximumallowed temperature. The thermal model can be representative of at leastone of a thermal capacity of content within the enclosed space, and athermal resistance of the cold storage enclosure. Determining anoperational schedule can be based on the thermal model, the energy costmodel, and a maximum allowed temperature for the cold storage facilitycan include identifying, based on the energy cost model, a first periodof time during which energy costs a first amount per unit, identifying,based on the energy cost model, a second period time preceding the firstperiod of time, during which energy costs a second amount per unit thatis less than the first amount per unit, adding information descriptiveof the second period of time to the operational schedule, theinformation being representative of time during which the refrigerationsystem is to be powered on to cool the enclosed space below the maximumallowed temperature, and adding information descriptive of the firstperiod of time to the operational schedule, the information beingrepresentative of time during which the enclosed space is allowed towarm toward the maximum allowed temperature. Determining a thermal modelof the enclosed space can include powering on the portion therefrigeration system based on the operational schedule, cooling, by thepowered portion of the refrigeration system, the enclosed space to atemperature below the maximum allowed temperature, reducing power usageof the powered portion of the refrigeration system based on theoperational schedule, determining a first plurality of temperaturelevels sensed by the plurality of temperature sensors, permitting theenclosed space to be warmed by ambient temperatures toward the maximumallowed temperature, determining a second plurality of temperaturelevels sensed by the plurality of temperature sensors, and determining athermal capacity of content of the enclosed space.

In a fourth aspect, a method for time shifting when a cold storagefacility is cooled includes determining a thermal model of a coldstorage facility comprising a cold storage enclosure that is configuredto be cooled by a refrigeration system and defining an enclosed space,obtaining an energy cost model that describes a schedule of variableenergy costs over a predetermined period of time in the future,determining an operational schedule for at least a portion of therefrigeration system based on the thermal model, the energy cost model,and a maximum allowed temperature for the enclosed space, and poweringon the portion the refrigeration system based on the operationalschedule, cooling, by the powered portion of the refrigeration system,the enclosed space to a temperature below the maximum allowedtemperature, reducing power usage of the powered portion of therefrigeration system based on the operational schedule, and permittingthe enclosed space to be warmed by ambient temperatures toward themaximum allowed temperature.

Various implementations can include some, all, or none of the followingfeatures. The method can also include determining a measured temperatureof the enclosed space, and powering on at least a portion of therefrigeration system based on the determined measured temperature and apredetermined threshold temperature value that is less than the maximumallowed temperature. The thermal model can be representative of at leastone of a thermal capacity of content within the enclosed space, and athermal resistance of the cold storage enclosure. Determining anoperational schedule based on the thermal model, the energy cost model,and a maximum allowed temperature for the cold storage facility caninclude identifying, based on the energy cost model, a first period oftime during which energy costs a first amount per unit, identifying,based on the energy cost model, a second period of time preceding thefirst period of time, during which energy costs a second amount per unitthat is less than the first amount per unit, adding informationdescriptive of the second period of time to the operational schedule,the information being representative of time during which therefrigeration system is to be powered on to cool the enclosed spacebelow the maximum allowed temperature, and adding informationdescriptive of the first period of time to the operational schedule, theinformation being representative of time during which the enclosed spaceis allowed to warm toward the maximum allowed temperature. Determining athermal model of the enclosed space can include powering on the portionthe refrigeration system based on the operational schedule, cooling, bythe powered portion of the refrigeration system, the enclosed space to atemperature below the maximum allowed temperature, reducing power usageof the powered portion of the refrigeration system based on theoperational schedule, determining a first plurality of temperaturelevels sensed by a plurality of temperature sensors, permitting theenclosed space to be warmed by ambient temperatures toward the maximumallowed temperature, determining a second plurality of temperaturelevels sensed by the plurality of temperature sensors, and determining athermal capacity of content of the enclosed space.

The disclosed systems and techniques may provide any of a variety ofadvantages. Time-shifted cooling strategies can introduce a variety ofefficiencies, which can be particularly relevant in the context ofcooled or refrigerated facilities, which have traditionally consumedlarge amounts of energy. For example, facilities can reduce and/oreliminate instances of a cooling system (and/or some of itssubcomponents) being toggled on and off, which can introduceinefficiencies as the system ramps up and down. With some conventionalfacilities, cooling systems may be run intermittently throughout theday, which can be inefficient. Instead of intermittently running suchsystems, those systems can be run in one (or more) longer andconsecutive stretches to bring the facility temperature down to a lowertemperature (below a setpoint), and can then be turned off or controlledto reduce power usage. Accordingly, inefficiencies around coolingsystems being turned on and off intermittently can be reduced and/oreliminated.

In another example, operational costs for refrigeration systems can bereduced. For instance, by having the ability to time-shift the use ofenergy, energy consumption during peak demand can be reduced and/oreliminated, and instead shifted to non-peak periods of time. This canreduce the operational cost of cooling a facility because energy duringpeak periods of time is generally more expensive than non-peak time.

In another example, time-shifting strategies used by one or morefacilities can, in aggregate, help to balance out energy demand forenergy producers and can also help energy producers avoid waste. Forinstance, energy producers are typically required to have sufficientenergy production capacity to meet variations in demand over time, whichcan result in energy producers often providing energy into the systemthat is ultimately wasted (unused), such as during non-peak hours of theday. By shifting energy consumption to non-peak hours, the amount ofenergy wasted across the system as a whole can be reduced, and also theproduction demands on energy producers during peak periods of time canbe reduced. The refrigeration system can also be made inherently moreefficient by shifting operation to certain (e.g., cooler) times of theday, so even if there is little or no imbalance between supply anddemand on the grid, value can still be derived through reduced powerconsumption.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will be apparent from the description and drawings, and fromthe claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic diagram that shows an example refrigerationmanagement system.

FIG. 2 is a graph of three example hourly power loads.

FIG. 3 is a graph of example temperature, example power use, and examplepower costs without precooling.

FIG. 4 is a graph of example temperature, example power use, and examplepower costs in an example in which precooling is used.

FIG. 5 is a conceptual diagram of a thermal model.

FIG. 6 is a block diagram of an example refrigeration management system.

FIG. 7 is a flow diagram of an example process for refrigerationmanagement.

FIG. 8 is a flow diagram of an example process for determining a thermalmodel.

FIG. 9 is a flow diagram of an example process for refrigerationschedule management.

FIG. 10 is a flow diagram of an example process for refrigerationschedule implementation.

FIG. 11 is a schematic diagram of an example of a generic computersystem.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

This document describes systems and techniques for systems andtechniques for refrigeration management, more specifically, for thereduction of the costs associated with powering the removal of heat fromcold storage facilities. The amount of power needed by a cold storagefacility can vary on a daily cycle due to the sun’s heat, outdoortemperatures, work shifts, etc. The demand on a utility providergenerally also varies on a daily cycle as well, and some utilityproviders use “peak pricing” and/or variable pricing in which the costof power goes up during times of high demand (e.g., summer mid-day), andgoes down for times of low demand (e.g., night).

In the realm of electrically powered facilities, batteries or flywheelscan be charged during off-peak periods to take advantage of lower,off-peak energy pricing, and discharged to power loads during on-peakperiods to avoid consuming power at relatively higher, on-peak rates.Somewhat analogously, this document describes processes in which coldstorage facilities are used as forms of thermal energy storage unitsthat can be “charged” (e.g., over-chilled) during low-price energyperiods and “discharged” (e.g., allowed to relax from the over-chilledstate) during high-price energy periods reduce or avoid the need forpower consumption during high-price periods while still keeping storedinventory at or below a predetermined temperature during the high-priceperiods.

In general, the cold storage facility can be pre-charged to abelow-normal cooled temperature using cheaper power and/or when thefacility is inherently more efficient to operate (e.g., cool hours,nighttime), and then be allowed to rise back closer to normal cooledtemperatures to reduce or avoid having to draw more expensive powerand/or operate during periods in which the facility is inherently lessefficient to operate (e.g., peak temperature hours, daytime). Forexample, a freezer warehouse may normally be kept at 0° F., but inanticipation of an upcoming peak-pricing period (e.g., mid-day tomorrowduring the warm season) the warehouse can be pre-cooled to -5° F. duringnighttime pricing. When the peak-pricing time arrives, at least aportion of the power demand and/or cost can be reduced by allowing thewarehouse to warm back toward 0° F. rather than by powering therefrigeration system using peak-priced power.

FIG. 1 is a schematic diagram that shows an example refrigerationmanagement system 100. A refrigeration facility 110 (e.g., cold storagefacility) includes a warehouse 112 is an insulated cold storageenclosure that defines a substantially enclosed space 114. The enclosedspace 114 has various content, including an inventory 120, a collectionof equipment 122 (e.g., forklifts, storage racks), and air. Theinventory 120, the equipment 122, and the air within the enclosed space114 has a thermal mass, as does the material of the warehouse 112 itself(e.g., steel supports, aluminum walls, concrete floors).

The enclosed space 114 is cooled by a refrigeration system 130 that iscontrolled by a controller 132 based on temperature feedback signalsfrom a collection of sensors 134 (e.g., temperature, humidity, airflow,motion). In some embodiments, the controller 132 can be a cold storagecontrol system controller, and can include a processor, memory, storage,inputs, and outputs. The sensors 134 are distributed throughout thewarehouse 112 to enable the controller 132 to monitor environmentalconditions throughout the enclosed space 114, and in some embodiments,in and/or near the inventory 120 (e.g., sensors embedded in or betweenboxes or pallets of stored goods). The controller 132 is configured toactivate the refrigeration system 130 based on feedback from the sensors134 to keep the enclosed space 114 at a temperature below apredetermined temperature limit. For example, an operator of therefrigeration facility 110 can agree to store a customer’s frozen foods(e.g., frozen meats, frozen French fries, ice cream) below a maximum of0° F.

The warehouse 112 is configured to resist heat infiltration. Heat energythat can raise the temperature of the enclosed space 114 and itscontents can come from a number of sources. A primary source of heatenergy is the sun 140, which can directly warm the structure of thewarehouse 112 and warms the ambient environment surrounding thewarehouse 112 and the refrigeration facility 110. Such heat energy caninfiltrate the warehouse 112 directly through the walls of the warehouse112 and/or through the opening of a door 124. Other sources of heatenergy can come from the operation of the equipment 122 (e.g., warmengines of forklifts, heat given off by lighting), the body heat ofhumans working within the enclosed space 114, and the inventory 120itself (e.g., fresh product may arrive at 20° F. for storage in a 0° F.freezer).

The controller 132 is in data communication with a scheduler 140 by anetwork 150 (e.g., the Internet, a cellular data network, a privatenetwork). In some embodiments, the scheduler 140 can be a cold storagemanagement server computer in communication with the controller 132. Insome phases of operation, the controller 132 collects measurements fromthe sensors 134 and time stamps based on a chronometer 136 (e.g., clock,timer) and provides that information to the scheduler 140. The scheduler140 uses such information to determine a thermal model of the warehouse112. An example process for the determination of thermal models will bediscussed further in the description of FIG. 8 .

In previous designs, temperature controllers generally monitor atemperature within a freezer to turn refrigeration systems on wheninternal temperatures exceed a preset temperature, and turn the systemsoff when the internal temperatures drop to slightly below the presettemperature. This range represents the hysteresis range for thecontroller under nominal operational conditions. Such operationalbehavior is discussed further in the description of FIG. 3 .

In the example of the system 100, the controller 132 receives anoperational schedule 138 from the scheduler 140. In general, theschedule 138 includes information that causes the controller 132 toprecool the enclosed space 114 to a temperature below the predeterminedtemperature limit for the inventory 120, and in some examples, below ahysteresis range for normal operation of the refrigeration system 130,during one or more predetermined periods of time. For example, undernominal operational conditions the controller 132 may be configured tokeep the enclosed space below 0° F. by turning the refrigeration system130 on when a temperature within the warehouse 112 exceeds -1° F., andturns the refrigeration system 130 off when the temperature drops below-2° F. However, the schedule 138 may configure the controller to coolthe enclosed space toward -5° F. or some other predetermined temperatureduring one or more predefined periods of time. As will be described inmore detail below, such periods of time can proceed periods of time inwhich the price of power is relatively higher (e.g., peak pricingperiods, periods of inherently low system efficiency).

The scheduler 140 is configured to determine one or more operationalschedules 142, of which the operational schedule 138 is one. Thescheduler 140 determines the operational schedules 142 based on thermalmodels. The scheduler receives thermal model information about therefrigeration facility 110, such as timed readings from the sensors 134and operational information about the refrigeration system 130, todetermine a thermal model of the warehouse 112. Determination of thermalmodels is discussed in more detail in the description of FIG. 8 .

The scheduler 140 also determines the operational schedules 142 based onan energy cost schedule 162 provided by a utility provider 160 thatprovides power to the refrigeration facility 110. The energy costschedule 162 includes information about the cost of energy at differenttimes and/or different days. For example, the utility provider 160 canbe an electric power provider that normally charges $0.12 perkilowatt-hour (kWh), but increases the cost to $0.20 per kilowatt-hourconsumed between 10am-2pm because demand for electrical power may peakduring that time. In another example, the utility provider 160 maycharge more during the summer months than during the winter months dueto the seasonal demand caused by air conditioners and other coolingsystems such as the refrigeration system 130. In general, the energycost schedule 162 describes one or more future cycles (e.g., daily)where power costs are scheduled to go up and down. Determination ofoperational schedules is discussed in more detail in the description ofFIG. 9 .

One or more other information providers 170 are configured to provideother information to the refrigeration facility 110, the scheduler 140,and/or the utility provider 160 over the network 150. For example, theinformation provider 170 can be a metrological service informationserver computer that provides daily or hourly weather forecasts. In suchan example, the utility provider 160 may use a forecast of hot weatherto predict increased demand and attempt to incentivize reduced demand byincreasing the cost of power during hot hours, and/or the scheduler 140may use the forecast to determine operational schedules 142 thatpre-chill the warehouse 112 in anticipation of hot weather thanincreased heat influx. In another example, the utility provider 160 mayprovide signals for demand response events, and/or the scheduler 140 mayuse the signals to modify operational schedules 142. In yet anotherexample, the information provider 170 can be a solar or wind energyprovider, and can provide a forecast of surplus solar or wind energy(e.g., a particularly sunny or windy day) that would be available topre-chill the warehouse 112.

In some embodiments, the information provider 170 can be a production orlogistics scheduler. For example, the information provider 170 mayprovide information to the scheduler 140 that indicates that a highlevel of activity may be planned for the warehouse 112 between 4pm and5pm tomorrow. Since high levels of activity may include increased outputof heat by the equipment 122 and workers, and more frequent or prolongedopenings of the door 124 that might alter the thermal model of thewarehouse 112. The scheduler 140 may respond by pre-chilling theenclosed space in anticipation of this predicted activity and thepredicted influx of heat.

In yet another example, the information provider 170 may provideinformation to the scheduler 140 about the inventory 120. Differenttypes of inventory can have different thermal characteristics. Forexample, a pallet of ice cream in plastic pails may absorb and releaseheat energy in different amounts and at different rates than a pallet ofcases of onion rings packaged in plastic bags within corrugatedcardboard boxes. In some embodiments, the scheduler 140 can useinformation about the thermal properties the inventory 120 or changes inthe inventory 120 to modify the thermal model and modify the operationalschedules 142 to account for changes to the thermal model. For example,the scheduler 140 prescribe a longer precooling period than usual whenthe inventory 120 includes items having unusually high thermalcapacities and/or items that are stored in well-insulated containers.

Different types of inventory can also enter the warehouse 112 indifferent states. For example, the information provider 170 may provideinformation to the scheduler 140 that indicates that a large inventoryof seafood at 10° F. is due to arrive at a 5° F. warehouse at 9amtomorrow. The scheduler 140 may modify the operational schedules 142 tooffset the effect cooling the seafood from the incoming 10° F. to thewarehouse’s setpoint of 5° F. while also anticipating and offsetting theeffects of variable energy pricing by prescribing a longer and/or colderperiod of pre-cooling.

FIG. 2 is a graph 200 of three example hourly power loads on a utilityprovider, such as the example utility provider 160 of FIG. 1 . A demandcurve 210 shows an example of average hourly power load for theMid-Atlantic region of the United States for the week of Jul. 7, 2009,when the average temperature was 85° F. A demand curve 220 shows anexample of average hourly power load for the Mid-Atlantic region of theUnited States for the week of Jan. 5, 2009, when the average temperaturewas 40° F. A demand curve 230 shows an example of average hourly powerload for the Mid-Atlantic region of the United States for the week ofApr. 6, 2009, when the average temperature was 55° F.

Each of the demand curves 210-230 shows that average hourly power loadsvaries on a substantially daily cycle, peaking around noon each day, andreaching a low point just after midnight each day. In the illustratedexample, each of the demand curves 210-230 starts on a Monday, and showsthat average hourly power loads varies on a substantially weekly cycle.For example, the demand curve 210 shows higher peak demands for thefirst five cycles of the week (e.g., the work week, peaking around47,000 MW around noon on Monday through Friday) and is on average lowerfor the sixth cycle of the week (e.g., peaking around 43,000 MW aroundnoon on Saturday) and even lower for the seventh cycle (e.g., peakingaround 38,000 MW Sunday, when even fewer businesses are open andconsuming power).

Power utilities generally build out their infrastructure in order toenough power to avoid brownouts and outages under as many circumstancedas practical. That generally means having enough power generatingcapacity to accommodate expected peak loads. However, during off-peaktimes the utility may have excess power generation capacity that isgoing unused while still incurring overhead costs. As such, utilityproviders may be incentivized to minimize excess power productioncapacity and maximize unused production capacity. One way that utilityproviders can do this is by incentivizing power consumers to reducetheir demand for power during peak times and possibly shift that demandto off-peak times. Customers can be incentivized by varying the cost ofpower consumption such that the price for power during peak times isrelatively higher, and the price during off-peak times is relativelylower.

FIG. 3 is a graph 300 of example temperature, example power use, andexample power costs without precooling. In some implementations, thegraph 300 can be an example of the behavior of a refrigeration facilitythat is not configured to use operational schedules such as the exampleoperational schedules 138, 142 of FIG. 1 . The graph 300 includes asubgraph 310 and a subgraph 350.

The subgraph 310 is a chart of an example temperature curve over anexample 24-hour period. In general, refrigeration systems do not run100% of the time, and unmanaged refrigeration systems cycle on and offbased on thermostatic control. The subgraph 310 shows an example uppertemperature limit 312 that is set slightly above -1° F., and a lowertemperature limit 314 set slightly below -1° F. The upper temperaturelimit 312 and the lower temperature limit 314 define an examplehysteresis for a thermostatic controller for a cold storage unit, suchas the controller 132 of the example refrigeration management system100. An air temperature curve 318 cycles approximately between the uppertemperature limit 312 and the lower temperature limit 314 and thethermostatic controller turns a refrigeration system on when the uppertemperature limit 312 is exceeded, and turns the refrigeration systemoff when the lower temperature limit 314 is reached. The air temperaturecurve 318 cycles around- 1° F., and maintains an inventory (e.g., frozenfood) temperature setpoint 320 substantially close to -1° F. In someembodiments, the inventory can have a greater thermal mass than air, andtherefore the inventory temperature can exhibit a dampened thermalresponse compared to the air that can provide an averaging effectrelative to the oscillations of the surrounding air temperature 318.

The subgraph 350 compares three other sets of data over the same 24-hourperiod as the subgraph 310. A weather temperature curve 352 shows anexample of how the temperature of ambient (e.g., outdoor) temperaturesvary during the example 24 hour period. A real-time price curve 354shows an example of how a power utility can vary the price of power(e.g., electricity) over the 24-hour period. As can be seen from thecurves 352 and 354, as the weather temperature 352 rises the real-timeprice 354 rises, albeit lagging slightly. In some examples, as theweather temperature 352 rises, power demand can rise with a delay (e.g.,possibly because outdoor temperatures could rise more quickly thanbuilding interiors, thereby causing a delay before air conditioningsystems and refrigeration systems would be thermostatically triggered),and such increased power demand may be disincentivized by the powerprovider by raising the cost of power during such peak times.

The subgraph 350 also shows a collection of power cost curves 356. Theareas underneath the power cost curves 356 represents the amount ofmoney consumed (e.g., cost) as part of consuming power, based on thereal time price 354, during various periods of time within the 24 hourperiod. For example, the areas under the power cost curves 356 can besummed to determine a total cost of the power consumed during theexample 24-hour period.

The power cost curves 356 correspond time wise with the drops in the airtemperature curve 318. For example, when the refrigeration system 130 isturned on, power is consumed as part of causing the air temperaturewithin the warehouse 114 to drop. In the illustrated example, the airtemperature curve 318 and the power cost curves 356 show a periodicity,with periods of power consumption lasting about 25 minutes approximatelyevery two hours. However, even though the duration of the powerconsumption cycles shown by the power consumption curves 356 are roughlyequal in length, they vary greatly in height. For example, a cycle 360has significantly less volume and therefore less total cost relative toa cycle 362. The difference in the costs between the cycles 360 and 362is substantially based on the difference in the real time price 354 atthe time of the cycle 360 and the relatively higher real time price 354at the time of the cycle 362.

As described earlier, the graph 300 shows an example of the behavior ofa refrigeration facility that is not configured to use operationalschedules such as the example operational schedules 138, 142 of FIG. 1 .For example, the graph 300 shows that power consumption occurs with asubstantially regular frequency regardless of the real time price 354.

FIG. 4 is a graph 400 of example temperature, example power use, andexample power costs in an example in which precooling is used. Ingeneral, refrigeration systems do not run 100% of the time, andunmanaged refrigeration systems cycle on and off based on thermostaticcontrol. However, refrigeration system, such as the examplerefrigeration system 100 of FIG. 1 , can use predetermined schedules inorder to shift their “on” times and “off” times to predetermined timesof the day in a way that can reduce operational costs. In someimplementations, the graph 400 can be an example of the behavior of arefrigeration facility that is configured to use operational schedulessuch as the example operational schedules 138, 142 of FIG. 1 . The graph400 includes a subgraph 410 and a subgraph 450.

The subgraph 410 is a chart of several temperature curves over anexample 24-hour period. An air temperature curve 418 varies as athermostatic controller turns a refrigeration system on and off. The airtemperature curve 418 cycles around- 1° F., and maintains an inventory(e.g., frozen food) temperature curve 420 substantially close to -1° F.In some embodiments, the inventory can have a greater thermal mass thanair, and therefore the inventory temperature 420 can exhibit a dampenedthermal response compared to the air that can provide an averagingeffect relative to the oscillations of the surrounding air temperature418.

The air temperature curve 418 includes a large drop 430 starting around2am and ending around 8am. The air temperature curve 418 also includes alarge rise 432 starting around 8am and continuing for the rest of theday. The inventory temperature 420 varies as well, but to a far lesserdegree (e.g., due to the relatively greater thermal capacity of solidmatter compared to air), varying by only a couple of tenths of a degreearound -1° F.

The subgraph 450 compares three other sets of data over the same 24-hourperiod as the subgraph 410. A weather temperature curve 452 shows anexample of how the temperature of ambient (e.g., outdoor) temperaturesvary during the example 24 hour period. A real-time price curve 454shows an example of how a power utility can vary the price of power(e.g., electricity) over the 24-hour period. As can be seen from thecurves 452 and 454, as the weather temperature 452 rises the real-timeprice 454 rises, albeit lagging slightly. In some examples, as theweather temperature 452 rises, power demand can rise with a delay (e.g.,possibly because outdoor temperatures could rise more quickly thanbuilding interiors, thereby causing a delay before air conditioningsystems and refrigeration systems would be thermostatically triggered),and such increased power demand may be disincentivized by the powerprovider by raising the cost of power during such peak times.

The subgraph 450 also shows a power cost curve 456. The area underneaththe power cost curve 456 represents the amount of money consumed (e.g.,cost) as part of consuming power, based on the real time price 454,during various periods of time within the 24 hour period. The area underthe power cost curve 456 can be summed to determine a total cost of thepower consumed during the example 24-hour period.

The power cost curve 456 corresponds time wise with the drop 430 in theair temperature curve 418. For example, when the refrigeration system130 is turned on, power is consumed as part of causing the airtemperature within the warehouse 114 to drop. Unlike the example graph300 of FIG. 3 , which shows power consumption that occurs with asubstantially regular frequency regardless of the real time price 354,the graph 400 shows that the power cost curve 456 is offset in advanceof a peak 455 in the real time price curve 454.

In the illustrated example, the power cost curve 456 occurs in advanceof the peak 455 due to an operational schedule, such as the exampleoperational schedule 138, provided by a scheduler such as the examplescheduler 140 and executed by a controller such as the examplecontroller 130 to precool an enclosed space and inventory such as theexample enclosed space 114 and the example inventory 120. In theillustrated example, an enclosed space is cooled and power is consumedduring a charging period 460 that proceeds a discharge period 462.

During the charging period 460, the air temperature 418 is cooled belowa nominal target temperature. For example, there may be a requirementthat the inventory temperature 420 not be allowed to rise able 0° F.,and therefore the corresponding refrigeration system may be configuredto thermostatically control the air temperature 418 to normally cyclearound -1° F., with a hysteresis of about +/- 0.2° F. However, duringthe charging period 460, the refrigeration system may be configured tocool the air temperature 418 toward approximately -3.5° F.

The charging period 460 occurs in advance of the peak 455 in the realtime price 454. As such, power consumption happens when power isrelatively less expensive (e.g., the height of the power cost curve 456is comparatively lower than the example power cost curve 356). Duringthe discharge period 462, the air temperature 418 is allowed to relaxback toward the ~-1° F. threshold, rather than consume power that ismore expensive during the peak 455 of the power cost curve 454. Byscheduling the charge period 460 (e.g., extra precooling during low-costpower times) and the discharge period 462 (e.g., allowing temperaturesto partly relax during high-cost power times), the total cost associatedwith the power cost curve 456 can be less than the total cost associatedwith unscheduled operations such as those represented by the sum of thepower cost curves 356.

FIG. 5 is a conceptual diagram of a thermal model 500 of the warehouse112 of the example refrigeration system 100 of FIG. 1 . In general, thethermal behavior of a refrigerated space can be mathematically modeledas a dampened harmonic oscillator. In some implementations, the thermalbehavior of a refrigerated space in response to powered cooling andpassive heating (e.g., heat intrusion) can mathematically approximatethe electrical behavior of a battery in response to powered charging andpassive discharge through a load (e.g., self-discharge). For example,the enclosed space 114 within the warehouse 112 can be “charged” byremoving an additional amount of heat energy (e.g., dropping thetemperature below the normal operating temperature, generally by usingelectrical power) from the air and the inventory 120, and can be“discharged” by allowing heat to infiltrate the enclosed space 114(e.g., until the normal operating temperature is reached).

The thermal model 500 can be determined at least partly by empiricalmeasurement. For example, the enclosed space 114 can start at an initialtemperature (e.g., -1° F.), and cooled to a predetermined lowertemperature (e.g., -5° F.). The cooled air and the inventory 120exchange thermal energy as the temperature changes. A collection oftemperature sensors distributed within the enclosed space 114 can bemonitored to determine when the enclosed space 114 has reached the lowertemperature. When the lower temperature has been reached and/orstabilized, the warehouse’s 112 refrigeration system can be partlyturned down or completely turned off (e.g., thereby reducing powerusage) and the sensors can be used to monitor the dynamic temperaturechanges across the enclosed space 114 as heat intrusion causes theenclosed space 114 to gradually warm (e.g., back toward -1° F.), withthe air and the inventory 120 absorbing some of the heat thatinfiltrates the enclosed space 114.

The rates at which the enclosed space 114 cools and warms can beanalyzed to estimate the thermal capacity and/or determine the thermalresistance of the warehouse 112. In some embodiments, the thermalcapacity can be based on the refrigeration capacity of the warehouse 112(e.g., the perturbance capacity of the system, the size of therefrigeration system 130), the volume of the air and the volumes and thetypes of materials that make up the inventory 120 (e.g., thermalcapacity of frozen fish versus frozen concentrated orange juice, paperpackaging versus metal packaging).

In some embodiments, the thermal resistance can be based on theinsulative qualities of the warehouse 112, the insulative qualities ofthe inventory 120 (e.g., stored in plastic vacuum sealed packages versuscorrugated cardboard boxes), heat given off by workers and/or equipmentwithin the warehouse 112, and the frequency with which doors to thewarehouse 112 are opened to ambient temperatures. In some embodiments,some or all of the terms of the thermal model 500 can be determined byperforming a thermal modeling cycle and monitoring the thermal responseof the warehouse 112. For example, if the thermal modeling cycle isperformed while a particular type and volume of the inventory 120 isstored, while particular amounts of equipment and workers are used inthe enclosed space 114, and while the doors to the enclosed space 114are opened and closed with a particular frequency, then the resultingthermal model can inherently include terms that reflect those variableswithout requiring these contributing factors to be determined ahead oftime.

The mathematical embodiment of the thermal model 500 takes the form ofdifferential equations such as:

$C_{f}\frac{dT_{f}}{dt} = - \alpha( {T_{f}(t) - T(t)} )$

And:

$C\frac{dT}{dt} = \alpha( {T_{f}(t) - T(t)} ) + \text{Φ}$

In which Φ represents net thermal flux, α represents the thermalcoupling coefficient between the food and air, C represents theeffective heat capacity of the air, C_(f) is the effective heat capacityof the inventory, T_(f) represents the temperature of the inventory, andT represents the temperature of the air.

The preceding equations can be solved analytically or numerically inorder to determine the time-dependent air and inventory temperature. Themodel is analogous to and approximates the dynamics of a dampened simpleharmonic oscillator. In thermal harmonic oscillator form, the precedingequations can be presented as:

$T(t) = A + mt + Be^{\frac{- t}{\tau}}$

And:

$T_{f}(t) = A + mt + B_{f}e^{\frac{- t}{\tau}}$

FIG. 6 is a block diagram of an example refrigeration management system600. The system 600 illustrates example interactions between a facility610 and a cloud-based algorithm 640. In some embodiments, the facility610 can be the refrigeration facility 110 of the example refrigerationmanagement system 100 of FIG. 1 . In some embodiments, the cloud-basedalgorithm 640 can be the scheduler 140.

The facility 610 includes a refrigeration system 612. In someembodiments, the refrigeration system can be configured to cool anenclosed space. For example, the refrigeration system 612 can be therefrigeration system 130.

The facility 610 includes an edge node controller 614 in communicationwith the refrigeration system 612. The edge node controller includes anexport module 616 and a setpoint module 618. Export module 616 isconfigured to export information received from the refrigeration system612, such as measured temperature values, temperature setpoint values,operational status information, and/or other information from therefrigeration system 612. The setpoint module 618 is configured toreceive operational schedules from the cloud-based algorithm 640. Insome embodiments, the context cluster 640 can be a server computersystem and the edge node controller 614 can be a client processorsystem. The edge node controller 614 is configured to perform functionsbased on the operational schedules, such as turning the refrigerationsystem 612 on and off (e.g., or to a reduced power configuration) atpredetermined times, and/or configuring temperature setpoints for therefrigeration system 612 at predetermined times.

The cloud-based algorithm 640 includes a feeds application programminginterface (API) 642. The feeds API 642 provides a programmaticcommunications endpoint that is configured to receive operationalinformation from the edge node controller 614. The operationalinformation includes timed temperature measurements from one or moresensors located throughout the refrigeration system 612. In someimplementations the operational information can also include informationsuch as refrigeration capacity information (e.g., a schedule thatindicates that 10% of the chillers used by the refrigeration system 612will be offline for maintenance tomorrow), operational volumeinformation (e.g., how full the warehouse is expected to be),operational status information (e.g., the facility 610 will be operatingwhen it is normally closed, and doors and equipment will be contributingheat when they normally would not, such as during a temporary secondwork shift or on a Sunday).

The feeds API 642 provides the information it receives to a flywheelingalgorithm 644. The flywheeling algorithm 644 also includes the convexoptimization logic that determines operational schedules for therefrigeration system 612. In general, the flywheeling algorithm 644determines operations schedules that can cause the refrigeration system612 to precool a cold storage space, and then allow the space to“flywheel”, “coast”, “discharge” or otherwise allow the temperature ofthe space to rise for a period of time without needing to consume powerin order to keep the storage space below a predetermined maximumtemperature limit.

The flywheeling algorithm 644 communicates with a thermal modellingalgorithm 646 that includes the software logic that determines thermalmodels for spaces, such as the spaces cooled by the refrigeration system612, based on the operational information received by the feeds API 642.The thermal modelling algorithm 646 is configured to store and retrievethermal models in a thermal models database 648. In someimplementations, the thermal models can be the example thermal model 500of FIG. 5 .

The flywheeling algorithm 644 communicates with a power rates API 650.The power rates API 650 provides a communications interface to a utilityprovider 652. The power rates API 650 enables the cloud-based algorithm640 to request and/or receive energy cost schedules from the utilityprovider 652. For example, the power rates API 650 could be used toreceive the energy cost schedule 162 of FIG. 1 from the utility provider160.

A historical data database 660 stores historical data that can beretrieved by the flywheeling algorithm 644. For example, the historicaldata database 660 can store multiple sets of operational information forthe facility 610 over time, and the flywheeling algorithm 640 can usesuch historical data as part of a process of determining operationalschedules. For example, the flywheeling algorithm 644 can look atmultiple sets of historical data to determine that the facility 610warms up more quickly on Mondays, has an average amount of warming onTuesdays-Fridays, and has little warming on Saturdays and Sundays (e.g.,Mondays may be heavy shipping days with lots of activity and dooropenings, and the facility 610 may be closed for business on weekendsand therefore have few to zero door openings). In another example, theflywheeling algorithm 644 can look at multiple sets of historical datato determine that the facility 610 warms up more quickly in the summerthan in the winter. The flywheeling algorithm 644 can use informationsuch as this to predict and/or improve estimations of the thermal modelof the facility 610 for various days, seasons, and other operationalvariables.

The flywheeling algorithm 644 uses the energy cost schedules received bythe power rates API 650, the thermal models determined by the thermalmodel algorithm 646, the operational information received by the feedsAPI 642, and the historical data retrieved from the historical datadatabase 660 to determine one or more operational schedules for therefrigeration system 612. For example, the flywheeling API 670 candetermine the operational schedules 138 and 142 of FIG. 1 .

A flywheeling API 670 provides a communication interface between thecloud-based algorithm 640 and the edge node controller 614. Theflywheeling API 670 can transmit operational schedules that are receivedby the setpoint getter 618. The edge node controller 614 usesoperational schedules received by the setpoint getter 618 to operate therefrigeration system 612. The operational schedules include informationthat can cause the edge node controller 614 to operate the refrigerationsystem 612 to chill a freezer or other enclosed space to a lowertemperature (e.g., pre-chilling, charging) during times when power isrelatively less expensive and/or when the refrigeration system 612 canbe operated more efficiently (e.g., during cooler hours), and allow thetemperatures to rise while not operating (e.g., discharging,flywheeling, coasting, relaxing) during other times when power isrelatively more expensive (e.g., peak pricing periods) and/or lessefficient (e.g., hot hours of the day).

FIG. 7 is a flow diagram of an example process 700 for refrigerationmanagement. In some implementations, the process 700 can be performed byparts or all of the example refrigeration management system 100 of FIG.1 or the example refrigeration management system 600 of FIG. 6 .

At 710, a thermal model of a cold storage facility comprising a coldstorage enclosure that is configured to be cooled by a refrigerationsystem and defining an enclosed space is determined. For example, thescheduler 140 can receive timed readings from the sensors 134 andoperational information about the refrigeration system 130, to determinea thermal model of the warehouse 112.

In some implementations, the thermal model can be representative of atleast one of the thermal capacity of content within the enclosed space,and the thermal resistance of the cold storage enclosure. For example,the air and the inventory 120 within the enclosed space 114 would have acombined thermal capacity, and the construction (e.g., insulativeproperties, areas of doors) of the warehouse 112 would contribute to thethermal resistance of the warehouse 112.

At 720, an energy cost model is obtained. The energy cost modeldescribes a schedule of variable energy costs over a predeterminedperiod of time in the future. For example, the scheduler is configuredto receive the energy cost schedule 162 from the utility provider 160.The energy cost schedule 162 includes information about the cost thatthe utility provider 160 charges for energy at different times and/ordifferent days.

At 730, an operational schedule is determined for at least a portion ofthe refrigeration system based on the thermal model, the energy costmodel, and a maximum allowed temperature for the enclosed space. Forexample, the scheduler 140 can determine the operational schedules 142based on an energy cost schedule 162, the nominal temperature setpointof the refrigeration facility 110, and the example thermal model 500 ofFIG. 5 .

In some implementations, determining the operational schedule based onthe thermal model, the energy cost model, and the maximum allowedtemperature for the cold storage facility can include identifying, basedon the energy cost model, a first period of time during which energycosts a first amount per unit, identifying, based on the energy costmodel, a second period time preceding the first period of time, duringwhich energy costs a second amount per unit that is less than the firstamount per unit, adding information descriptive of the second period oftime to the operational schedule, the information being representativeof time during which the refrigeration system is to be powered on tocool the enclosed space below the maximum allowed temperature; andadding information descriptive of the first period of time to theoperational schedule, the information being representative of timeduring which the enclosed space is allowed to warm toward the maximumallowed temperature. For example, the scheduler 140 can analyze theenergy cost schedule 162 to identify a period of time in which theper-unit cost of power (e.g., dollars per kilowatt hour for electricity)is relatively high, and then identify another period of time in whichthe per-unit cost of power is relatively lower and precedes thehigh-cost period (e.g., identify a low price period that occurs before apeak price period). The scheduler 140 can then determine that at least aportion of the low-price period is to be used for chilling the enclosedspace 114 an additional amount below the nominal temperature setpoint.The scheduler 140 can also determine that the refrigeration system 130should not be operated any more than necessary to maintain the maximumtemperature setpoint of the inventory 120. As such, the schedule cancause the controller 132 to provide the enclosed space 114 with an extrathermal charge of cooling using cheap power so the inventory can staybelow the maximum temperature for at least a while without consumingexpensive power.

In some implementations, determining the thermal model of the enclosedspace can include powering on the portion the refrigeration system basedon the operational schedule, cooling, by the powered portion of therefrigeration system, the enclosed space to a temperature below themaximum allowed temperature, reducing power consumption of the poweredportion of the refrigeration system based on the operational schedule,determining a first plurality of temperature levels sensed by theplurality of temperature sensors, permitting the enclosed space to bewarmed by ambient temperatures toward the maximum allowed temperature,determining a second plurality of temperature levels sensed by theplurality of temperature sensors, and determining a thermal capacity ofcontent of the enclosed space. For example, the controller 132 can turnthe refrigeration system 130 on and keep it on until a predeterminedcondition is set, such as by setting the temperature setpoint to atemperature below what the enclosed space 114 will reach in a practicalamount of time (e.g., -20° F.) to cause the refrigeration system 130 torun substantially constantly for a predetermined amount of time. Inanother example, the controller 132 can run the refrigeration system 130until a predetermined temperature (e.g., -6° F.) has been reached and/orstabilized. The controller 132 can then shut the refrigeration system130 off (e.g., or reduce power usage) and start recording thetemperatures sensed by the sensors 134 to over time as the enclosedspace 114 is allowed to warm. The controller 132 and/or the scheduler140 can process the timed temperature measurements to determine thethermal model 500.

In some implementations, determining the operational schedule can bebased on demand charges. Demand charges are somewhat analogous to aspeeding ticket. The utility can charge a fee (i.e., demand charge)based on the maximum power draw for the month, and in some examples thisfee can be as much as 50% of the power bill. The scheduler 140 can beconfigured to account for such fees when determining the schedule, inorder to prevent too much of the refrigeration equipment from turning onat once even when power rates are relatively low.

At 740, the operational schedule is performed. In some implementations,the operational schedule can include powering on a portion of therefrigeration system based on the operational schedule, cooling, by thepowered portion of the refrigeration system, the enclosed space to atemperature below the maximum allowed temperature, reducing power usageof the powered portion of the refrigeration system based on theoperational schedule, and permitting the enclosed space to be warmed byambient temperatures toward the maximum allowed temperature. Forexample, based on the operational schedule 138, the controller 132 cancause the refrigeration system 130 to cool the enclosed space 114 by anadditional amount below the nominal temperature setpoint during a periodof time during which the utility provider 160 charges a relativelylesser price for power, and stops the additional cooling and allows theenclosed space 114 to warm back toward the predetermined nominaltemperature threshold during a period of time during which the utilityprovider 160 charges a relatively greater price for power.

In some implementations, the process 700 can also include determining ameasured temperature of the enclosed space, and powering on at least aportion of the refrigeration system based on the determined measuredtemperature and a predetermined threshold temperature value that is lessthan the maximum allowed temperature. For example, the controller 132can allow the enclosed space 114 to warm back toward a predeterminedmaximum temperature (e.g., from -4.1° F. to a limit of -1.3° F.) andonce the predetermined maximum temperature is approached, therefrigeration system 130 can resume normal operations (e.g., consumingpower as needed in order to keep the enclosed space 114 at or below-1.3° F.).

FIG. 8 is a flow diagram of an example process 800 for determining athermal model. In some implementations, the process 800 can be theexample step 710 of FIG. 7 . In some implementations, the process 800can be performed by parts or all of the example refrigeration managementsystem 100 of FIG. 1 or the example refrigeration management system 600of FIG. 6 . In some implementations, the process 800 can be used todetermine the example thermal model 500 of FIG. 5 .

At 802, a refrigeration system is powered on. For example, thecontroller 132 can configure the refrigeration system 130 to power on bysetting the target temperature to -4° F.

At 804, an enclosed space is cooled to a predetermined temperature. Forexample, the enclosed space 114 and the inventory 120 can be cooled to-4° F.

At 806, the refrigeration system is turned off. For example, thecontroller 132 can configure the refrigeration system 130 to power offby setting the target temperature to -1° F. In some implementations, therefrigeration system can be put into a reduced power consumptionconfiguration instead of being turned off. For example, half orthree-quarters of the chillers in a system can be turned off while theremainder are left powered on. In another example, some or all of therefrigeration system can be modulated (e.g., pulsed) to operate only inseveral-minute intervals when needed.

At 808, temperature sensor data is obtained. At 810, the temperaturesensor and time data is recorded. For example, the controller 132 canmonitor the sensors 134 to record temperature readings from within theenclosed space 114 along with timestamp information based on thechronometer 136.

At 812, a determination is made. If the temperature of the enclosedspace is below a predetermined maximum temperature setpoint (e.g.,chosen to prevent the inventory 120 from getting too warm), then theenclosed space is allowed to continue warming at 814. If the temperatureof the enclosed space is not below the predetermined maximum temperaturesetpoint, then refrigeration resumes at 816 (e.g., the refrigerationsystem 130 is turned back on).

At 818, the stored temperature and time data is analyzed to determine athermal model of the enclosed space. For example, the controller 132and/or the scheduler 140 can process the collected timestampedtemperature readings of the warming enclosed space 114 to determine thethermal model 500.

FIG. 9 is a flow diagram of an example process 900 for refrigerationschedule management. In some implementations, the process 900 can be theexample step 730 of FIG. 7 . In some implementations, the process 900can be performed by parts or all of the example refrigeration managementsystem 100 of FIG. 1 or the example refrigeration management system 600of FIG. 6 .

At 902, an energy cost model is obtained. For example, the scheduler 140can query or otherwise request the energy cost model 162 (e.g., theexample energy cost curve 454 of FIG. 4 ) from the utility provider 160.

At 904, a future period of time is identified. The period of time isidentified based on times associated with relatively high energy costs.For example, the scheduler 140 can identify the peak 455 and designate aperiod of time that includes the peak 455 at the discharge period 462.

At 906, a second future period of time is identified. The second periodof time is based on times associated with relatively low energy coststhat precedes the identified high energy cost time period and/or demandcharges. For example, the scheduler 140 can identify the period of timebefore the discharge period 462 as a charge period 460.

At 908, a thermal model, a maximum temperature limit value, and aminimum temperature limit value are obtained. For example, thecontroller 908 can obtain or determine the thermal model 500, andreceive information about the highest and lowest temperatures that areallowed for the inventory 120. For example, some high-fat ice creamproducts can be best stored at -20° F. (e.g., establishing a maximumallowable temperature), but may be stored in plastic containers thatbecome exceptionally brittle at -40° F. (e.g., establishing a minimumallowable temperature), and these temperatures can be used as thethermal boundaries used by the controller 132 for normal operations aswell as precooling operations. In some implementations, the thermalmodel can be the output of the example process 800 of FIG. 8 .

At 910, a lower temperature is determined that offsets warming duringthe high energy cost time period. For example, air temperature 418 isnormally kept around -0.5° F., but the scheduler 140 can determine thatthe temperature of the enclosed space 114 could rise by about 3° F.during the discharge period 462, and drop the normal operatingtemperature of -0.5° F. by about -3° F. to about -3.5° F.

At 912, at least part of the identified low energy cost time period isidentified as a precooling period based on the determined lowertemperature. For example, the scheduler 140 can determine that therefrigeration system 130 will require six hours before the peak 455 todrop the temperature of the air in the enclosed space 114 from -0.5° F.to about -3.5° F.

At 914, the precooling period is added to an operational schedule. Forexample, the charge period 460 can be identified in the operationalschedule 138 as a future time for precooling the enclosed space 114.

FIG. 10 is a flow diagram of an example process for refrigerationschedule implementation. In some implementations, the process 1000 canbe the example step 740 of FIG. 7 . In some implementations, the process1000 can be performed by parts or all of the example refrigerationmanagement system 100 of FIG. 1 or the example refrigeration managementsystem 600 of FIG. 6 .

At 1002, an operational schedule is received. For example, thecontroller 132 can receive the operational schedule 138 from thescheduler 140.

At 1004, a determination is made. If the temperature of an enclosedspace is not below a predetermined maximum threshold temperature, then arefrigeration system is powered on at 1006 and the enclosed space iscooled at 1008. For example, of the enclosed space 114 reaches 0° F.when the thermostatic setpoint of the refrigeration system 130 is -1°F., then the refrigeration system 130 can turn on to cool the enclosedspace 114. The process 1000 continues at 1002.

If at 1004 the temperature of an enclosed space is below thepredetermined maximum threshold temperature, then another determinationis made at 1010. If it is not time to precool the enclosed space, thenthe process continues at 1002. For example, if the chronometer 136indicates that the current time is not a time that is identified by theoperational schedule 138 as a precooling (e.g., charging) time, then thecontroller 132 can check for a new operational schedule and/or continuemonitoring the time and temperature of the enclosed space 114.

If at 1010 it is time to precool, then another determination is made at1012. If the temperature of the enclosed space is above a predeterminedprecooling temperature, then the refrigeration system is powered on at1006. For example, if the chronometer 136 indicates that the currenttime is a time that is identified by the operational schedule 138 as aprecooling (e.g., charging) time, then the controller 132 can set thetemperature setpoint of the warehouse 120 to -4° F., and if thetemperature of the enclosed space 114 is above the setpoint, therefrigeration system 130 can be turned on to cool the enclosed space114.

If the temperature of the enclosed space is not above the predeterminedprecooling temperature, then the refrigeration system is powered off orput into a reduced power mode at 1014, and the enclosed space is allowedto warm at 1016. For example, the enclosed space 114 can be held at thepredetermined lower precooling temperature of -4° F. until theprecooling period ends.

Many of the previous examples have described in terms of reducing costsassociated with operating refrigeration systems such as the examplesystem 100 of FIG. 1 , however the described pre-chilling techniques canbe used for other purposes as well. In some embodiments, utilityproviders may use hydroelectric or wind power to provide much of thepower to a grid, and then engage fossil fuel based generators (e.g.,that are easily and quickly started up an shut down) to augment thatpower capacity during peak periods. As such, during periods of peakpower usage, the environmental impact of power consumption can berelatively greater than at non-peak times of the day. The techniquesdescribed herein can enable refrigeration management systems to reducetheir dependence on peak, possibly more polluting, power generationsystems, and perform more of their operations using power fromrelatively “greener” power sources. In some embodiments, suchenvironmental savings can be traded as a financial instrument (e.g.,trading carbon credits). In some embodiments, the precooling techniquesdescribed herein can be used to sell back excess power to utilityproviders. For example, the facility 110 can have a contractualagreement with the utility provider 160 to consume 5 kWh of power perday, and that the utility provider 160 will compensate the facility 110for every Watt of power the facility 110 does not consume of the agreed5 kWh. From the perspective of the facility 110, the facility 110 cansell unused power back to the utility provider 160, possibly at a profit(e.g., by pre-chilling during some parts of the day to avoid powerconsumption during other parts of the day).

FIG. 11 is a schematic diagram of an example of a generic computersystem 1100. The system 1100 can be used for the operations described inassociation with the method 300 according to one implementation. Forexample, the system 1100 may be included in either or all of thecontroller 132, the refrigeration system 130, the scheduler 140, theutility provider 160, the other information provider 170, the edge nodecontroller 614, and the context cluster 640..

The system 1100 includes a processor 1110, a memory 1120, a storagedevice 1130, and an input/output device 1140. Each of the components1110, 1120, 1130, and 1140 are interconnected using a system bus 1150.The processor 1110 is capable of processing instructions for executionwithin the system 1100. In one implementation, the processor 1110 is asingle-threaded processor. In another implementation, the processor 1110is a multi-threaded processor. The processor 1110 is capable ofprocessing instructions stored in the memory 1120 or on the storagedevice 1130 to display graphical information for a user interface on theinput/output device 1140.

The memory 1120 stores information within the system 1100. In oneimplementation, the memory 1120 is a computer-readable medium. In oneimplementation, the memory 1120 is a volatile memory unit. In anotherimplementation, the memory 1120 is a non-volatile memory unit.

The storage device 1130 is capable of providing mass storage for thesystem 1100. In one implementation, the storage device 1130 is acomputer-readable medium. In various different implementations, thestorage device 1130 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device.

The input/output device 1140 provides input/output operations for thesystem 1100. In one implementation, the input/output device 1140includes a keyboard and/or pointing device. In another implementation,the input/output device 1140 includes a display unit for displayinggraphical user interfaces.

The features described can be implemented in digital electroniccircuitry, or in computer hardware, firmware, software, or incombinations of them. The apparatus can be implemented in a computerprogram product tangibly embodied in an information carrier, e.g., in amachine-readable storage device for execution by a programmableprocessor; and method steps can be performed by a programmable processorexecuting a program of instructions to perform functions of thedescribed implementations by operating on input data and generatingoutput. The described features can be implemented advantageously in oneor more computer programs that are executable on a programmable systemincluding at least one programmable processor coupled to receive dataand instructions from, and to transmit data and instructions to, a datastorage system, at least one input device, and at least one outputdevice. A computer program is a set of instructions that can be used,directly or indirectly, in a computer to perform a certain activity orbring about a certain result. A computer program can be written in anyform of programming language, including compiled or interpretedlanguages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment.

Suitable processors for the execution of a program of instructionsinclude, by way of example, both general and special purposemicroprocessors, and the sole processor or one of multiple processors ofany kind of computer. Generally, a processor will receive instructionsand data from a read-only memory or a random access memory or both. Theessential elements of a computer are a processor for executinginstructions and one or more memories for storing instructions and data.Generally, a computer will also include, or be operatively coupled tocommunicate with, one or more mass storage devices for storing datafiles; such devices include magnetic disks, such as internal hard disksand removable disks; magneto-optical disks; and optical disks. Storagedevices suitable for tangibly embodying computer program instructionsand data include all forms of non-volatile memory, including by way ofexample semiconductor memory devices, such as EPROM, EEPROM, and flashmemory devices; magnetic disks such as internal hard disks and removabledisks; magneto-optical disks; and CD-ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,ASICs (application-specific integrated circuits).

To provide for interaction with a user, the features can be implementedon a computer having a display device such as a CRT (cathode ray tube)or LCD (liquid crystal display) monitor for displaying information tothe user and a keyboard and a pointing device such as a mouse or atrackball by which the user can provide input to the computer.

The features can be implemented in a computer system that includes aback-end component, such as a data server, or that includes a middlewarecomponent, such as an application server or an Internet server, or thatincludes a front-end component, such as a client computer having agraphical user interface or an Internet browser, or any combination ofthem. The components of the system can be connected by any form ormedium of digital data communication such as a communication network.Examples of communication networks include, e.g., a LAN, a WAN, and thecomputers and networks forming the Internet.

The computer system can include clients and servers. A client and serverare generally remote from each other and typically interact through anetwork, such as the described one. The relationship of client andserver arises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications are possible. For example, the logic flows depictedin the figures do not require the particular order shown, or sequentialorder, to achieve desirable results. In addition, other steps may beprovided, or steps may be eliminated, from the described flows, andother components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

1-20. (canceled)
 21. A method for automatically and proactivelyadjusting energy consumption schedules in a facility for performingfacility operations, the method comprising: receiving temperature datain near real-time from sensors positioned throughout a facility;receiving energy grid information in near real-time from a utilityprovider remote from the facility; obtaining a logistics schedule forthe facility that identifies one or more operations to be performed inthe facility over a future period of time; retrieving historicoperational data of the facility over one or more past periods of time,wherein the historic operation data includes at least a targettemperature set point at which to maintain at least a portion of thefacility while the logistics schedule is performed in the facility;generating one or more predictions based on processing the temperaturedata, the energy grid information, the logistics schedule, and thehistoric operational data, wherein the one or more predictions compriseat least one time segment over the future period of time at which energycosts are less than a threshold level; determining an operationalschedule based on the one or more predictions, wherein the operationalschedule comprises instructions to perform one or more actions in thelogistics schedule during the at least one time segment over the futureperiod of time at which energy costs are less than the threshold level;and controlling components in the facility according to the operationalschedule at the at least one time segment.
 22. The method of claim 21,wherein controlling components in the facility comprises activating arefrigeration system to cool at least the portion of the facility to thetarget temperature set point during the at least one time segment. 23.The method of claim 21, wherein controlling components in the facilitycomprises deactivating a refrigeration system to raise a temperature inat least the portion of the facility to the target temperature set pointwithout consuming energy provided by the utility provider.
 24. Themethod of claim 23, wherein the deactivating the refrigeration system isperformed at a second time segment over the future period of time atwhich the energy costs are predicted to be greater than the thresholdlevel.
 25. The method of claim 21, wherein the one or more actionsinclude preparing items for outbound delivery from the facility.
 26. Themethod of claim 21, wherein the one or more actions include putting awayitems in the facility into designated storage locations.
 27. The methodof claim 21, wherein the energy grid information includes at least oneof projected energy costs over the future period of time and projectedenergy demand over the future period of time.
 28. The method of claim21, wherein the historic operational data indicates actions performed inthe facility over the one or more past periods of time during one ormore energy conditions, the one or more energy conditions including atleast one of: an increased demand for energy by the facility, anincreased demand for energy across an energy grid, a decreased supply ofenergy available at the energy grid, increased energy costs, decreasedenergy costs, variable changes in weather, and variable changes inenergy availability from the utility provider.
 29. The method of claim21, wherein the utility provider comprises an energy grid that providesenergy from renewable energy resources.
 30. The method of claim 21,further comprising: receiving near real-time weather data; anddetermining the at least one time segment over the future period of timeat which the energy costs are less than the threshold value basedfurther in part on the near real-time weather data.
 31. The method ofclaim 21, further comprising: receiving, from a plurality of sensors,real-time signals about weather conditions; processing the signals; andbased on processing the signals, determining likelihoods that theweather conditions will reduce the energy costs at various time segmentsover the future period of time; and adjusting the operational scheduleto perform the one or more actions during one of the various timesegments having a likelihood that the weather conditions will reduce theenergy costs.
 32. The method of claim 21, wherein the logistics scheduleidentifies inventory expected to arrive at or be distributed from thefacility over the future period of time.
 33. The method of claim 21,further comprising: determining an amount of energy to be added to thefacility by the logistics schedule over the future period of time; anddetermining the operational schedule further based on the determinedamount of energy to be added to the facility.
 34. The method of claim21, wherein determining an operational schedule based on the one or morepredictions comprises offsetting a predetermined quantity of inbounddeliveries to a night shift during the at least one time segment when(i) the energy costs are less than the threshold level, (ii) an expectedenergy demand is less than a threshold demand level, or (iii) anexternal ambient temperature is less than a threshold ambienttemperature.
 35. The method of claim 21, wherein determining anoperational schedule based on the one or more predictions comprisesproactively activating a refrigeration system in the facility to coolthe facility to the target temperature set point before a projectedfuture time segment at which an external ambient temperature isprojected to exceed a threshold ambient temperature.
 36. The method ofclaim 21, wherein the energy grid information includes weather forecastsfor a geographic location associated with at least one of the facilityand the utility provider.
 37. The method of claim 21, wherein the energygrid information includes a forecast of surplus solar energy.
 38. Themethod of claim 21, wherein the energy grid information includes aforecast of surplus wind energy.
 39. The method of claim 21, furthercomprising adjusting the operational schedule based on thermalproperties of inventory expected to arrive at or be distributed from thefacility over the future period of time.
 40. A cold storage facilitycomprising: a cold storage enclosure defining an enclosed space; atleast one temperature sensor configured to detect temperature signals inthe enclosed space; a refrigeration system configured to cool theenclosed space; and a control system comprising: a data processingapparatus; a communication subsystem that transmits and receives dataover one or more networks and one or more media; a memory device storinginstructions that when executed by data processing apparatus cause thecontrol system to perform operations comprising: receiving temperaturedata in near real-time from the at least one temperature sensor;receiving energy grid information in near real-time from a controller ofa utility provider remote from the cold storage facility; obtaining alogistics schedule for the cold storage facility that identifies one ormore operations to be performed in the cold storage facility over afuture period of time; retrieving historic operational data of the coldstorage facility over one or more past periods of time, wherein thehistoric operation data includes at least a target temperature set pointat which to maintain at least the enclosed space while the logisticsschedule is performed in the cold storage facility; generating one ormore predictions based on processing the temperature data, the energygrid information, the logistics schedule, and the historic operationaldata, wherein the one or more predictions comprise at least one timesegment over the future period of time at which energy costs are lessthan a threshold level; determining an operational schedule based on theone or more predictions, wherein the operational schedule includesinstructions to perform one or more actions in the logistics scheduleduring the at least one time segment over the future period of time atwhich energy costs are less than the threshold level; and controllingthe refrigeration system according to the operational schedule at the atleast one time segment.
 41. The cold storage facility of claim 40,wherein the energy grid information includes an energy cost model thatdescribes a schedule of variable energy costs over the future period oftime.